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首页> 外文会议>2016 International Conference on Biometrics >Accurate iris segmentation in non-cooperative environments using fully convolutional networks
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Accurate iris segmentation in non-cooperative environments using fully convolutional networks

机译：使用完全卷积网络在非合作环境中进行准确的虹膜分割

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摘要

Conventional iris recognition requires controlled conditions (e.g., close acquisition distance and stop-and-stare scheme) and high user cooperation for image acquisition. Non-cooperative acquisition environments introduce many adverse factors such as blur, off-axis, occlusions and specular reflections, which challenge existing iris segmentation approaches. In this paper, we present two iris segmentation models, namely hierarchical convolutional neural networks (HCNNs) and multi-scale fully convolutional network (MFCNs), for noisy iris images acquired at-a-distance and on-the-move. Both models automatically locate iris pixels without handcrafted features or rules. Moreover, the features and classifiers are jointly optimized. They are end-to-end models which require no further pre- and post-processing and outperform other state-of-the-art methods. Compared with HCNNs, MFCNs take input of arbitrary size and produces correspondingly-sized output without sliding window prediction, which makes MFCNs more efficient. The shallow, fine layers and deep, global layers are combined in MFCNs to capture both the texture details and global structure of iris patterns. Experimental results show that MFCNs are more robust than HCNNs to noises, and can greatly improve the current state-of-the-arts by 25.62% and 13.24% on the UBIRIS.v2 and CASIA.v4-distance databases, respectively.
机译：常规的虹膜识别需要受控条件（例如，近距离采集距离和停止并凝视方案）以及用于图像采集的高度用户合作。非合作采集环境引入了许多不利因素，例如模糊，离轴，遮挡和镜面反射，这些挑战挑战了现有的虹膜分割方法。在本文中，我们提出了两种虹膜分割模型，分别是分层卷积神经网络（HCNN）和多尺度全卷积网络（MFCN），用于在远处和移动中获得的嘈杂虹膜图像。两种模型都可以自动定位虹膜像素，而无需手工特征或规则。而且，特征和分类器被共同优化。它们是端到端模型，不需要进一步的前处理和后处理，并且性能优于其他最新技术。与HCNN相比，MFCN接受任意大小的输入并产生相应大小的输出，而无需滑动窗口预测，这使MFCN更加高效。浅层，精细层和深层，全局层在MFCN中组合在一起，以捕获虹膜图案的纹理细节和全局结构。实验结果表明，MFCN比HCNN具有更强的抗噪能力，并且可以在UBIRIS.v2和CASIA.v4距离数据库上分别将当前的最新技术水平分别提高25.62％和13.24％。

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  • 来源
  • 会议地点 Halmstad(SE)
  • 作者

    Nianfeng Liu; Haiqing Li; Man Zhang; Jing Liu; Zhenan Sun; Tieniu Tan;

  • 作者单位

    Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190;

    Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190;

    Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190;

    Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190;

    Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190;

    Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Iris recognition; Iris; Image segmentation; Neurons; Kernel; Feature extraction; Neural networks;

    机译：虹膜识别;虹膜;图像分割;神经元;核;特征提取;神经网络;

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